{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures 7 and 8 (Main Text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded. Shape: (1599624, 94)\n",
      "   challenger  target    year  dispute  dispute2  target_resist   id  \\\n",
      "0         2.0    20.0  1945.0      0.0       0.0            NaN  1.0   \n",
      "1         2.0    20.0  1946.0      0.0       0.0            NaN  1.0   \n",
      "2         2.0    20.0  1947.0      0.0       0.0            NaN  1.0   \n",
      "3         2.0    20.0  1948.0      0.0       0.0            NaN  1.0   \n",
      "4         2.0    20.0  1949.0      0.0       0.0            NaN  1.0   \n",
      "\n",
      "   atopally_target  defense_target  milex_challenger  ...  size_t_num2  \\\n",
      "0              1.0             1.0        90000000.0  ...          0.0   \n",
      "1              0.0             0.0        45133984.0  ...          0.0   \n",
      "2              0.0             0.0        14315999.0  ...          0.0   \n",
      "3              0.0             0.0        10960998.0  ...          0.0   \n",
      "4              1.0             1.0        13503000.0  ...        238.0   \n",
      "\n",
      "   size_t_b  dispute_hh3  dispute2_hh3  dispute_hh4  dispute2_hh4  \\\n",
      "0       0.0          0.0           0.0          0.0           0.0   \n",
      "1       0.0          0.0           0.0          0.0           0.0   \n",
      "2       0.0          0.0           0.0          0.0           0.0   \n",
      "3       0.0          0.0           0.0          0.0           0.0   \n",
      "4       1.0          0.0           0.0          0.0           0.0   \n",
      "\n",
      "   mutiny_only  violent_only  size_only  duration_only  \n",
      "0          0.0           0.0        0.0            0.0  \n",
      "1          0.0           0.0        0.0            0.0  \n",
      "2          0.0           0.0        0.0            0.0  \n",
      "3          0.0           0.0        0.0            0.0  \n",
      "4          1.0           0.0        1.0            1.0  \n",
      "\n",
      "[5 rows x 94 columns]\n",
      "\n",
      "==============================\n",
      "Running analysis at Dyad-Level (id)\n",
      "==============================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of group-time ATT estimates: 2043\n",
      "      g       t  event_time       att        se  n_treated  n_control\n",
      "0  1945  1945.0         0.0  0.105142  0.014392         58       3958\n",
      "1  1945  1990.0        45.0 -0.000739  0.003487         57      17607\n",
      "2  1945  1991.0        46.0 -0.002113  0.004002         57      21031\n",
      "3  1945  1992.0        47.0 -0.001946  0.003811         57      21273\n",
      "4  1945  1993.0        48.0 -0.002551  0.003458         57      22484\n",
      "\n",
      "===== GTATE-Style Aggregated ATT (Dyad-Level) =====\n",
      "ATT (GTATE)     : 0.000544\n",
      "SE (GTATE)      : 0.000054\n",
      "95% CI          : [0.000438, 0.000650]\n",
      "\n",
      "First 10 ATT(g, t) estimates (Dyad-Level):\n",
      "      g       t  event_time       att        se  n_treated  n_control\n",
      "0  1945  1945.0         0.0  0.105142  0.014392         58       3958\n",
      "1  1945  1990.0        45.0 -0.000739  0.003487         57      17607\n",
      "2  1945  1991.0        46.0 -0.002113  0.004002         57      21031\n",
      "3  1945  1992.0        47.0 -0.001946  0.003811         57      21273\n",
      "4  1945  1993.0        48.0 -0.002551  0.003458         57      22484\n",
      "5  1945  1994.0        49.0 -0.002644  0.003831         57      22434\n",
      "6  1945  1995.0        50.0 -0.002976  0.003859         57      22035\n",
      "7  1945  1996.0        51.0 -0.004002  0.004082         57      21131\n",
      "8  1945  1997.0        52.0 -0.001512  0.004344         57      20533\n",
      "9  1945  1998.0        53.0 -0.002027  0.003597         57      20253\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==============================\n",
      "Running analysis at Target-Level (target)\n",
      "==============================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of group-time ATT estimates: 1713\n",
      "      g       t  event_time       att        se  n_treated  n_control\n",
      "0  1945  1945.0         0.0  0.105142  0.014392         58       3958\n",
      "1  1945  1990.0        45.0 -0.000571  0.002284        160      15568\n",
      "2  1945  1991.0        46.0 -0.000539  0.002847        176      18127\n",
      "3  1945  1992.0        47.0 -0.004993  0.002528        180      18179\n",
      "4  1945  1993.0        48.0 -0.004100  0.002144        185      19048\n",
      "\n",
      "===== GTATE-Style Aggregated ATT (Target-Level) =====\n",
      "ATT (GTATE)     : 0.000474\n",
      "SE (GTATE)      : 0.000045\n",
      "95% CI          : [0.000387, 0.000562]\n",
      "\n",
      "First 10 ATT(g, t) estimates (Target-Level):\n",
      "      g       t  event_time       att        se  n_treated  n_control\n",
      "0  1945  1945.0         0.0  0.105142  0.014392         58       3958\n",
      "1  1945  1990.0        45.0 -0.000571  0.002284        160      15568\n",
      "2  1945  1991.0        46.0 -0.000539  0.002847        176      18127\n",
      "3  1945  1992.0        47.0 -0.004993  0.002528        180      18179\n",
      "4  1945  1993.0        48.0 -0.004100  0.002144        185      19048\n",
      "5  1945  1994.0        49.0 -0.003049  0.002508        186      18970\n",
      "6  1945  1995.0        50.0 -0.004132  0.002430        186      18596\n",
      "7  1945  1996.0        51.0 -0.005674  0.002584        186      17851\n",
      "8  1945  1997.0        52.0 -0.002623  0.002672        186      17480\n",
      "9  1945  1998.0        53.0 -0.002666  0.002077        186      17297\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ================================================\n",
    "# 1. Lightweight installer if packages are missing\n",
    "# ================================================\n",
    "try:\n",
    "    import pandas as _pd\n",
    "    import statsmodels as _sm\n",
    "    import matplotlib as _mpl\n",
    "except Exception:\n",
    "    import sys, subprocess\n",
    "    subprocess.check_call(\n",
    "        [\n",
    "            sys.executable, \"-m\", \"pip\", \"install\", \"-U\",\n",
    "            \"pandas\", \"statsmodels\", \"matplotlib\", \"seaborn\", \"pyreadstat\"\n",
    "        ]\n",
    "    )\n",
    "\n",
    "# ================================================\n",
    "# 2. Imports\n",
    "# ================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "# ================================================\n",
    "# 3. Load dataset\n",
    "# ================================================\n",
    "file_path = Path(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Mutiny_Conflict Paper_All\\Replication File\\jcr_mutiny_conflict_data.dta\"\n",
    ")\n",
    "\n",
    "try:\n",
    "    df = pd.read_stata(file_path)\n",
    "except Exception as e:\n",
    "    print(\"Error reading Stata file:\", e)\n",
    "    raise\n",
    "\n",
    "print(\"Data loaded. Shape:\", df.shape)\n",
    "print(df.head())\n",
    "\n",
    "# ================================================\n",
    "# 4. Common covariates list\n",
    "# ================================================\n",
    "covariates = [\n",
    "    'lmmilex_persol_t',\n",
    "    'years_since_coup',\n",
    "    'ucdp_civconf_t2',\n",
    "    'defense_target',\n",
    "    'capprop',\n",
    "    'jdem',\n",
    "    'majorpower',\n",
    "    'cold_war'\n",
    "]\n",
    "\n",
    "# Quick check\n",
    "for v in ['dispute'] + covariates:\n",
    "    if v not in df.columns:\n",
    "        raise ValueError(f\"Variable '{v}' not found in the dataset.\")\n",
    "\n",
    "# ================================================\n",
    "# 5. Helper: run event-study + GTATE for a given unit\n",
    "# ================================================\n",
    "def run_event_study_for_unit(df, unit_var, label_for_plots):\n",
    "    \"\"\"\n",
    "    unit_var: column used as 'unit' (e.g., 'id' or 'target')\n",
    "    label_for_plots: string used in plot titles (e.g., 'Dyad-Level', 'Target-Level')\n",
    "    \"\"\"\n",
    "    print(f\"\\n==============================\")\n",
    "    print(f\"Running analysis at {label_for_plots} ({unit_var})\")\n",
    "    print(f\"==============================\")\n",
    "\n",
    "    # --------------------------------------------\n",
    "    # 5.1 Define first treatment year & event time\n",
    "    # --------------------------------------------\n",
    "    # Treatment is mutiny_t == 1\n",
    "    first_treat = (\n",
    "        df[df['mutiny_t'] == 1]\n",
    "        .groupby(unit_var)['year']\n",
    "        .min()\n",
    "        .reset_index()\n",
    "    )\n",
    "    first_treat.columns = [unit_var, 'first_treat_year_tmp']\n",
    "\n",
    "    df_local = pd.merge(df, first_treat, on=unit_var, how='left')\n",
    "    df_local['first_treat_year_tmp'] = df_local['first_treat_year_tmp'].fillna(0).astype(int)\n",
    "\n",
    "    # Event time: year relative to first treatment\n",
    "    df_local['event_time_tmp'] = df_local['year'] - df_local['first_treat_year_tmp']\n",
    "    # never-treated -> NA\n",
    "    df_local.loc[df_local['first_treat_year_tmp'] == 0, 'event_time_tmp'] = np.nan\n",
    "\n",
    "    # --------------------------------------------\n",
    "    # 5.2 Pre-treatment ATT (event-study) & pretrend plot\n",
    "    # --------------------------------------------\n",
    "    event_times = sorted(df_local['event_time_tmp'].dropna().unique())\n",
    "    att_results = []\n",
    "\n",
    "    for t in event_times:\n",
    "        if t >= 0:\n",
    "            continue  # only pre-treatment years\n",
    "\n",
    "        # Treated observations at this relative event time\n",
    "        treated = df_local[df_local['event_time_tmp'] == t]\n",
    "\n",
    "        # Control: never-treated in the same calendar years\n",
    "        control = df_local[\n",
    "            (df_local['first_treat_year_tmp'] == 0) &\n",
    "            (df_local['year'].isin(treated['year'].unique()))\n",
    "        ]\n",
    "\n",
    "        if len(treated) < 5 or len(control) < 5:\n",
    "            continue\n",
    "\n",
    "        temp = pd.concat([treated, control]).copy()\n",
    "\n",
    "        # D = 1 if the unit will be treated later (i.e., has first_treat_year_tmp > year)\n",
    "        temp['D'] = (\n",
    "            (temp['first_treat_year_tmp'] > temp['year']) &\n",
    "            (temp['first_treat_year_tmp'] > 0)\n",
    "        ).astype(int)\n",
    "\n",
    "        # Design matrix with covariates\n",
    "        X_vars = ['D'] + covariates\n",
    "        temp_clean = (\n",
    "            temp[['dispute'] + X_vars]\n",
    "            .replace([np.inf, -np.inf], np.nan)\n",
    "            .dropna()\n",
    "        )\n",
    "\n",
    "        if temp_clean['D'].nunique() < 2:\n",
    "            continue\n",
    "\n",
    "        X = sm.add_constant(temp_clean[X_vars].astype(float))\n",
    "        y = temp_clean['dispute'].astype(float)\n",
    "\n",
    "        model = sm.OLS(y, X).fit()\n",
    "\n",
    "        att_results.append({\n",
    "            'event_time': t,\n",
    "            'att': model.params['D'],\n",
    "            'se': model.bse['D']\n",
    "        })\n",
    "\n",
    "    att_df = pd.DataFrame(att_results).sort_values('event_time')\n",
    "\n",
    "    # Restrict to a chosen window (e.g., -50 to -1)\n",
    "    pretrend_df = att_df[(att_df['event_time'] >= -50) & (att_df['event_time'] < 0)]\n",
    "\n",
    "    if pretrend_df.empty:\n",
    "        print(f\"No pre-treatment event-time estimates for {label_for_plots}.\")\n",
    "    else:\n",
    "        # Extract arrays\n",
    "        att = pretrend_df['att'].to_numpy()\n",
    "        se = pretrend_df['se'].to_numpy()\n",
    "        x = pretrend_df['event_time'].to_numpy()\n",
    "\n",
    "        # Confidence intervals\n",
    "        ci_low_95 = att - 1.96 * se\n",
    "        ci_high_95 = att + 1.96 * se\n",
    "        ci_low_90 = att - 1.645 * se\n",
    "        ci_high_90 = att + 1.645 * se\n",
    "\n",
    "        # -------- Pretrend plot --------\n",
    "        plt.figure(figsize=(5, 4))\n",
    "        ax = plt.gca()\n",
    "        ax.set_facecolor('#f5f5f5')\n",
    "\n",
    "        # 90% CI band\n",
    "        plt.fill_between(\n",
    "            x, ci_low_90, ci_high_90,\n",
    "            color='#c6dbef', alpha=0.4, label='90% CI', linewidth=0\n",
    "        )\n",
    "\n",
    "        # 95% CI band\n",
    "        plt.fill_between(\n",
    "            x, ci_low_95, ci_high_95,\n",
    "            color='#6baed6', alpha=0.3, label='95% CI', linewidth=0\n",
    "        )\n",
    "\n",
    "        # ATT line\n",
    "        plt.plot(x, att, color='#08306B', lw=1.5, label='ATT Estimate')\n",
    "\n",
    "        # Reference line at 0\n",
    "        plt.axhline(0, color='black', linestyle='--', lw=1)\n",
    "\n",
    "        # Labels\n",
    "        plt.xlabel(\"Event time\", fontsize=11)\n",
    "        plt.ylabel(\"Event Study Estimate\", fontsize=11)\n",
    "        plt.title(f\"Pretreatment Trend (Treatment Timing: {label_for_plots})\", fontsize=11)\n",
    "        plt.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "        plt.ylim(pretrend_df['att'].min() - 0.006, pretrend_df['att'].max() + 0.006)\n",
    "        plt.tight_layout()\n",
    "        plt.legend(frameon=False, loc='lower right')\n",
    "\n",
    "        for spine in ['top', 'right', 'left', 'bottom']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    # --------------------------------------------\n",
    "    # 5.3 GTATE-style aggregated ATT (group-time ATT)\n",
    "    # --------------------------------------------\n",
    "    treated_groups = sorted(\n",
    "        df_local.loc[df_local['first_treat_year_tmp'] > 0, 'first_treat_year_tmp'].unique()\n",
    "    )\n",
    "    periods = sorted(df_local['year'].unique())\n",
    "\n",
    "    gt_results = []\n",
    "\n",
    "    for g in treated_groups:\n",
    "        for t in periods:\n",
    "            # Only post-treatment periods for group g\n",
    "            if t < g:\n",
    "                continue\n",
    "\n",
    "            # Treated units: cohort g in year t\n",
    "            treated = df_local[\n",
    "                (df_local['first_treat_year_tmp'] == g) &\n",
    "                (df_local['year'] == t)\n",
    "            ]\n",
    "            if treated.empty:\n",
    "                continue\n",
    "\n",
    "            # Control: never-treated or not-yet-treated at time t\n",
    "            control = df_local[\n",
    "                ((df_local['first_treat_year_tmp'] == 0) |\n",
    "                 (df_local['first_treat_year_tmp'] > t)) &\n",
    "                (df_local['year'] == t)\n",
    "            ]\n",
    "\n",
    "            if len(treated) < 5 or len(control) < 5:\n",
    "                continue\n",
    "\n",
    "            temp = pd.concat([treated, control]).copy()\n",
    "            temp['D'] = (temp['first_treat_year_tmp'] == g).astype(int)\n",
    "\n",
    "            cols_needed = ['D', 'dispute'] + covariates\n",
    "            temp_clean = (\n",
    "                temp[cols_needed]\n",
    "                .replace([np.inf, -np.inf], np.nan)\n",
    "                .dropna()\n",
    "            )\n",
    "\n",
    "            if temp_clean['D'].nunique() < 2:\n",
    "                continue\n",
    "\n",
    "            X = sm.add_constant(temp_clean[['D'] + covariates].astype(float))\n",
    "            y = temp_clean['dispute'].astype(float)\n",
    "\n",
    "            model = sm.OLS(y, X).fit()\n",
    "\n",
    "            gt_results.append({\n",
    "                'g': g,\n",
    "                't': t,\n",
    "                'event_time': t - g,\n",
    "                'att': model.params['D'],\n",
    "                'se': model.bse['D'],\n",
    "                'n_treated': len(treated),\n",
    "                'n_control': len(control)\n",
    "            })\n",
    "\n",
    "    gt_df = pd.DataFrame(gt_results)\n",
    "    print(\"\\nNumber of group-time ATT estimates:\", gt_df.shape[0])\n",
    "    print(gt_df.head())\n",
    "\n",
    "    # Keep only post-treatment event times\n",
    "    gt_df_post = gt_df[gt_df['event_time'] >= 0].copy()\n",
    "    if gt_df_post.empty:\n",
    "        raise ValueError(f\"No post-treatment (g, t) cells found after filtering for {label_for_plots}.\")\n",
    "\n",
    "    # Weights: share of treated units in each (g, t)\n",
    "    gt_df_post['weight'] = gt_df_post['n_treated'] / gt_df_post['n_treated'].sum()\n",
    "\n",
    "    # Aggregated ATT (GTATE-style)\n",
    "    agg_att = np.sum(gt_df_post['weight'] * gt_df_post['att'])\n",
    "\n",
    "    # Approximate variance assuming independence\n",
    "    agg_var = np.sum((gt_df_post['weight'] ** 2) * (gt_df_post['se'] ** 2))\n",
    "    agg_se = np.sqrt(agg_var)\n",
    "\n",
    "    ci_low = agg_att - 1.96 * agg_se\n",
    "    ci_high = agg_att + 1.96 * agg_se\n",
    "\n",
    "    print(f\"\\n===== GTATE-Style Aggregated ATT ({label_for_plots}) =====\")\n",
    "    print(f\"ATT (GTATE)     : {agg_att:.6f}\")\n",
    "    print(f\"SE (GTATE)      : {agg_se:.6f}\")\n",
    "    print(f\"95% CI          : [{ci_low:.6f}, {ci_high:.6f}]\")\n",
    "\n",
    "    print(f\"\\nFirst 10 ATT(g, t) estimates ({label_for_plots}):\")\n",
    "    print(gt_df_post[['g', 't', 'event_time', 'att', 'se', 'n_treated', 'n_control']].head(10))\n",
    "\n",
    "    # --------------------------------------------\n",
    "    # 5.4 Single ATT dot + CI plot\n",
    "    # --------------------------------------------\n",
    "    fig, ax = plt.subplots(figsize=(8, 2.5))\n",
    "    ax.set_facecolor('#f9f9f9')\n",
    "\n",
    "    for spine in ['top', 'right', 'left', 'bottom']:\n",
    "        ax.spines[spine].set_visible(False)\n",
    "\n",
    "    # ATT dot and 95% CI error bar\n",
    "    ax.errorbar(\n",
    "        x=agg_att,\n",
    "        y=0,\n",
    "        xerr=1.96 * agg_se,\n",
    "        fmt='o',\n",
    "        color='black',\n",
    "        ecolor='gray',\n",
    "        elinewidth=2,\n",
    "        capsize=4,\n",
    "        markersize=8\n",
    "    )\n",
    "\n",
    "    # Vertical dashed line at 0\n",
    "    ax.axvline(0, color='gray', linestyle='--', linewidth=1)\n",
    "\n",
    "    # Always include 0 and add padding\n",
    "    span = ci_high - ci_low if ci_high > ci_low else abs(agg_att) + 1e-6\n",
    "    pad = 0.1 * span\n",
    "    xmin = min(ci_low, 0) - pad\n",
    "    xmax = max(ci_high, 0) + pad\n",
    "    ax.set_xlim(xmin, xmax)\n",
    "\n",
    "    ax.set_xlabel(\"ATT\", fontsize=13)\n",
    "    ax.set_title(\n",
    "        f\"Average Treatment Effect on the Treated ({label_for_plots})\",\n",
    "        fontsize=12\n",
    "    )\n",
    "    ax.set_yticks([])\n",
    "    ax.set_ylim(-0.5, 0.5)\n",
    "    ax.grid(True, linestyle=':', linewidth=0.7, alpha=0.6)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# ================================================\n",
    "# 6. Run for both units: id (dyad) and target (country)\n",
    "# ================================================\n",
    "# Dyad-level (your original)\n",
    "run_event_study_for_unit(df, unit_var='id', label_for_plots='Dyad-Level')\n",
    "\n",
    "# Target-country–level\n",
    "run_event_study_for_unit(df, unit_var='target', label_for_plots='Target-Level')\n"
   ]
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